{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "94ba7940",
   "metadata": {},
   "source": [
    "# AlexNet Demo Summary\n",
    "\n",
    "- 数据集: 我使用flower数据集，这是一个很小的数据集\n",
    "- 参数调节: 调参后，在测试集上的预测准确率可达 72% 左右\n",
    "    - 在 flower 数据集使用默认参数训练会导致严重的过拟合，以及发生震荡。调参后过拟合问题得到显著改善，震荡缓解。<br>\n",
    "    \n",
    "    调参操作如下:\n",
    "    - 降低 learning rate\n",
    "    - 增大 weight_decay\n",
    "    - 网络初始化使用 HeUniform\n",
    "    - 调大 batch size\n",
    "    - 使用 early stop\n",
    "- 数据增强: 数据增强后，测试集上预测准确率可达 77% 左右\n",
    "    - 数据增强增加了不同的训练样本数量，测试集准确率有显著提高. 增加数据是抵抗过拟合的好方法之一。\n",
    "    - 将数据增强层放到模型中，训练会变得非常慢。我采用的方案可能是先对数据做预处理，再训练，不把预处理放到训练中。\n",
    "- 使用多 GPU 会变慢。可能是数据量太少的原因？毕竟使用多 GPU 会带来一些 overhead\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "046598f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-10 14:49:11.370850: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F AVX512_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-04-10 14:49:11.492021: I tensorflow/core/util/port.cc:104] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2023-04-10 14:49:12.129753: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/huangboxiang/.local/clang/lib:/home/huangboxiang/.local/clang/lib/clang/13.0.0/lib/linux:/home/huangboxiang/.local/gcc/lib64:/home/huangboxiang/.local/gcc/lib/gcc/x86_64-pc-linux-gnu/10.3.0:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64/:\n",
      "2023-04-10 14:49:12.129831: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/huangboxiang/.local/clang/lib:/home/huangboxiang/.local/clang/lib/clang/13.0.0/lib/linux:/home/huangboxiang/.local/gcc/lib64:/home/huangboxiang/.local/gcc/lib/gcc/x86_64-pc-linux-gnu/10.3.0:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64/:\n",
      "2023-04-10 14:49:12.129839: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# hyper-parameters\n",
    "\n",
    "batch_size = 128\n",
    "\n",
    "# image resizing\n",
    "img_height = 227\n",
    "img_width = 227\n",
    "\n",
    "# data augmentation\n",
    "do_augment = True\n",
    "augment_expand = 10\n",
    "\n",
    "# train\n",
    "min_epochs = 10\n",
    "max_epochs = 30\n",
    "\n",
    "# early stop\n",
    "early_stop_patience = 5\n",
    "early_stop_monitor='val_loss'\n",
    "\n",
    "# optimizations\n",
    "learning_rate = 5e-5\n",
    "if do_augment:\n",
    "    learning_rate = learning_rate / (augment_expand + 1) * 1.1\n",
    "\n",
    "weight_decay = 2e-3\n",
    "use_ema = True\n",
    "initializer = lambda: tf.keras.initializers.HeUniform(seed=np.random.randint(1, 1000))\n",
    "\n",
    "# distribution\n",
    "use_mirror_strategy = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "829a5e4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def alexnet_model(input_shape, category_num,\n",
    "                  learning_rate=1e-3,\n",
    "                  weight_decay=1e-4,\n",
    "                  use_ema=False,\n",
    "                  initializer = lambda: initializers.GlorotUniform(seed=np.random.randint(1, 1000))):\n",
    "    model = tf.keras.models.Sequential([\n",
    "        layers.Input(shape=input_shape),\n",
    "        \n",
    "        # 第一层卷积\n",
    "        layers.Conv2D(96, (11, 11), strides=(4, 4),\n",
    "                      activation='relu',\n",
    "                      kernel_initializer=initializer()),\n",
    "        layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2)),\n",
    "        layers.BatchNormalization(),\n",
    "        # 第二层卷积\n",
    "        layers.Conv2D(256, (5, 5), strides=(1, 1), padding='same',\n",
    "                      activation='relu',\n",
    "                      kernel_initializer=initializer()),\n",
    "        layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2)),\n",
    "        layers.BatchNormalization(),\n",
    "        # 第三层卷积\n",
    "        layers.Conv2D(384, (3, 3), strides=(1, 1), padding='same',\n",
    "                      activation='relu', \n",
    "                      kernel_initializer=initializer()),\n",
    "        layers.BatchNormalization(),\n",
    "        # 第四层卷积\n",
    "        layers.Conv2D(384, (3, 3), strides=(1, 1), padding='same',\n",
    "                      activation='relu', \n",
    "                      kernel_initializer=initializer()),\n",
    "        layers.BatchNormalization(),\n",
    "        # 第五层卷积\n",
    "        layers.Conv2D(256, (3, 3), strides=(1, 1), padding='same',\n",
    "                      activation='relu', \n",
    "                      kernel_initializer=initializer()),\n",
    "        layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2)),\n",
    "        layers.BatchNormalization(),\n",
    "        # 将多维数据展开成一维数据\n",
    "        layers.Flatten(),\n",
    "        # 第一个全连接层\n",
    "        layers.Dense(4096, activation='relu', kernel_initializer=initializer()),\n",
    "        layers.Dropout(0.5),\n",
    "        # 第二个全连接层\n",
    "        layers.Dense(4096, activation='relu', kernel_initializer=initializer()),\n",
    "        layers.Dropout(0.5),\n",
    "        # 输出层，分类输出\n",
    "        layers.Dense(category_num,\n",
    "                     activation='softmax',\n",
    "                     kernel_initializer=initializer())\n",
    "    ], name='alexnet_demo')\n",
    "    model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=learning_rate,\n",
    "                                                     weight_decay=weight_decay,\n",
    "                                                     use_ema=use_ema),\n",
    "                  loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),\n",
    "                  metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9531f049",
   "metadata": {},
   "outputs": [],
   "source": [
    "def resizing_and_rescaling(img_height, img_width):\n",
    "    return tf.keras.models.Sequential([\n",
    "        layers.Resizing(img_height, img_width),\n",
    "        layers.Rescaling(1./255, input_shape=(img_height, img_width, 3)),\n",
    "    ])\n",
    "\n",
    "def data_augmentation(img_height, img_width):\n",
    "    return tf.keras.models.Sequential([\n",
    "        layers.Input(shape=(img_height, img_width, 3)),\n",
    "        layers.RandomFlip(\"horizontal_and_vertical\"),\n",
    "        layers.RandomRotation(0.1),\n",
    "        layers.RandomZoom(0.1),\n",
    "\n",
    "        # many different kinds of image augmentations\n",
    "        #layers.RandomFlip(\"horizontal_and_vertical\"),\n",
    "        #layers.RandomRotation(0.2),\n",
    "        #layers.RandomZoom(0.2),\n",
    "        #layers.RandomTranslation(0.2, 0.2),\n",
    "        #layers.RandomContrast(0.1),\n",
    "        #layers.RandomBrightness(0.1),\n",
    "        #layers.RandomCrop(height=img_height, width=img_width)\n",
    "    ])\n",
    "\n",
    "def dataset_len(ds):\n",
    "    i = 0\n",
    "    for e in ds:\n",
    "        i += e[0].shape[0]\n",
    "    return i\n",
    "\n",
    "def prepare(ds, img_height, img_width, shuffle=True, augment=False, augment_expand=1):\n",
    "    AUTOTUNE = tf.data.AUTOTUNE\n",
    "    \n",
    "    layer_preproc = resizing_and_rescaling(img_height, img_width)\n",
    "    \n",
    "    layer_augment = data_augmentation(img_height, img_width)\n",
    "    \n",
    "    # rescale all datasets.\n",
    "    ds = ds.map(\n",
    "        lambda x, y: (layer_preproc(x), y),\n",
    "        num_parallel_calls=AUTOTUNE\n",
    "    )\n",
    "    \n",
    "    # Use data augmentation only on the training set.\n",
    "    aug_ds = None\n",
    "    if augment:\n",
    "        for i in range(augment_expand):\n",
    "            if not aug_ds:\n",
    "                aug_ds = ds.map(\n",
    "                    lambda x, y: (layer_augment(x, training=True), y), \n",
    "                    num_parallel_calls=AUTOTUNE\n",
    "                )\n",
    "            else:\n",
    "                aug_ds = aug_ds.concatenate(\n",
    "                    ds.map(lambda x, y: (layer_augment(x, training=True), y),\n",
    "                           num_parallel_calls=AUTOTUNE\n",
    "                ))\n",
    "        ds = ds.concatenate(aug_ds)\n",
    "        \n",
    "    if shuffle:\n",
    "        ds = ds.shuffle(10000)\n",
    "        \n",
    "    # Cache all datasets\n",
    "    ds = ds.cache()\n",
    "        \n",
    "    # Use buffered prefetching on all datasets.\n",
    "    return ds.prefetch(buffer_size=AUTOTUNE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "21630e9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data_dir=/home/huangboxiang/.keras/datasets/flower_photos, image_count=3670\n",
      "Found 3670 files belonging to 5 classes.\n",
      "Using 2936 files for training.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-10 14:49:16.050541: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 22284 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:3b:00.0, compute capability: 8.6\n",
      "2023-04-10 14:49:16.051814: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 22284 MB memory:  -> device: 1, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:5e:00.0, compute capability: 8.6\n",
      "2023-04-10 14:49:16.052895: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:2 with 22284 MB memory:  -> device: 2, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:86:00.0, compute capability: 8.6\n",
      "2023-04-10 14:49:16.054033: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:3 with 22152 MB memory:  -> device: 3, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:af:00.0, compute capability: 8.6\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 3670 files belonging to 5 classes.\n",
      "Using 734 files for validation.\n",
      "class_names=['daisy', 'dandelion', 'roses', 'sunflowers', 'tulips']\n",
      "Orig train set len=2936\n",
      "Orig val set len=734\n",
      "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting ImageProjectiveTransformV3 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting ImageProjectiveTransformV3 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:From /home/huangboxiang/py_venv/lib/python3.9/site-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23.\n",
      "Instructions for updating:\n",
      "Lambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089\n",
      "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting ImageProjectiveTransformV3 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting ImageProjectiveTransformV3 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting ImageProjectiveTransformV3 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting ImageProjectiveTransformV3 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting ImageProjectiveTransformV3 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting ImageProjectiveTransformV3 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting ImageProjectiveTransformV3 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting ImageProjectiveTransformV3 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting ImageProjectiveTransformV3 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting ImageProjectiveTransformV3 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting ImageProjectiveTransformV3 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting ImageProjectiveTransformV3 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting ImageProjectiveTransformV3 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting ImageProjectiveTransformV3 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting ImageProjectiveTransformV3 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting ImageProjectiveTransformV3 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting ImageProjectiveTransformV3 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting ImageProjectiveTransformV3 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting ImageProjectiveTransformV3 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting ImageProjectiveTransformV3 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting ImageProjectiveTransformV3 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting ImageProjectiveTransformV3 cause there is no registered converter for this op.\n",
      "Augmented train set len=32296\n",
      "Augmented val set len=734\n",
      "images shape=(128, 227, 227, 3)\n",
      "label shape=(128,)\n",
      "image max value=0.9997888207435608, min value=0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load datasets\n",
    "\n",
    "import pathlib\n",
    "dataset_url = \"https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz\"\n",
    "data_dir = tf.keras.utils.get_file('flower_photos', origin=dataset_url, untar=True)\n",
    "data_dir = pathlib.Path(data_dir)\n",
    "image_count = len(list(data_dir.glob('*/*.jpg')))\n",
    "print(f'data_dir={data_dir}, image_count={image_count}')\n",
    "\n",
    "train_ds = tf.keras.preprocessing.image_dataset_from_directory(\n",
    "  data_dir,\n",
    "  validation_split=0.2,\n",
    "  subset=\"training\",\n",
    "  seed=123,\n",
    "  #image_size=(img_height, img_width),\n",
    "  batch_size=batch_size\n",
    ")\n",
    "\n",
    "val_ds = tf.keras.preprocessing.image_dataset_from_directory(\n",
    "  data_dir,\n",
    "  validation_split=0.2,\n",
    "  subset=\"validation\",\n",
    "  seed=123,\n",
    "  #image_size=(img_height, img_width),\n",
    "  batch_size=batch_size\n",
    ")\n",
    "class_names = train_ds.class_names\n",
    "print(f'class_names={class_names}')\n",
    "\n",
    "# 只对训练集做数据增强\n",
    "train_ds = prepare(train_ds, img_height, img_width,\n",
    "                   shuffle = True,\n",
    "                   augment = do_augment, augment_expand=augment_expand)\n",
    "val_ds = prepare(val_ds, img_height, img_width,\n",
    "                 shuffle = False, augment = False)\n",
    "\n",
    "for images, labels in train_ds.take(1):\n",
    "    print(f'images shape={images.shape}')\n",
    "    print(f'label shape={labels.shape}')\n",
    "    max_val = np.max(images[0])\n",
    "    min_val = np.min(images[0])\n",
    "    print(f'image max value={max_val}, min value={min_val}')\n",
    "    assert(max_val <= 1.0 and min_val >= 0)\n",
    "    plt.imshow(images[0])\n",
    "    plt.axis('off')\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2eec11a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"alexnet_demo\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 55, 55, 96)        34944     \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 27, 27, 96)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " batch_normalization (BatchN  (None, 27, 27, 96)       384       \n",
      " ormalization)                                                   \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 27, 27, 256)       614656    \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 13, 13, 256)      0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " batch_normalization_1 (Batc  (None, 13, 13, 256)      1024      \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 13, 13, 384)       885120    \n",
      "                                                                 \n",
      " batch_normalization_2 (Batc  (None, 13, 13, 384)      1536      \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " conv2d_3 (Conv2D)           (None, 13, 13, 384)       1327488   \n",
      "                                                                 \n",
      " batch_normalization_3 (Batc  (None, 13, 13, 384)      1536      \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " conv2d_4 (Conv2D)           (None, 13, 13, 256)       884992    \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 6, 6, 256)        0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " batch_normalization_4 (Batc  (None, 6, 6, 256)        1024      \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 9216)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 4096)              37752832  \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 4096)              0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 4096)              16781312  \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 4096)              0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 5)                 20485     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 58,307,333\n",
      "Trainable params: 58,304,581\n",
      "Non-trainable params: 2,752\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "if use_mirror_strategy:\n",
    "    strategy = tf.distribute.MirroredStrategy()\n",
    "    with strategy.scope():\n",
    "        model = alexnet_model(\n",
    "            input_shape = (img_height, img_width, 3),\n",
    "            category_num=len(class_names),\n",
    "            learning_rate=learning_rate,\n",
    "            weight_decay=weight_decay,\n",
    "            use_ema = use_ema,\n",
    "            initializer = initializer,\n",
    "        )\n",
    "else:\n",
    "    model = alexnet_model(\n",
    "        input_shape = (img_height, img_width, 3),\n",
    "        category_num=len(class_names),\n",
    "        learning_rate=learning_rate,\n",
    "        weight_decay=weight_decay,\n",
    "        use_ema = use_ema,\n",
    "        initializer = initializer,\n",
    "    )\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "455a7bbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# callbacks\n",
    "\n",
    "# early stopping\n",
    "early_stop_callback = tf.keras.callbacks.EarlyStopping(\n",
    "    monitor=early_stop_monitor,\n",
    "    patience=early_stop_patience,\n",
    "    restore_best_weights=False,\n",
    "    start_from_epoch = min_epochs,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0e2b4eff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-10 14:50:00.382159: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:428] Loaded cuDNN version 8201\n",
      "2023-04-10 14:50:02.907470: I tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:630] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "253/253 [==============================] - 33s 97ms/step - loss: 2.1159 - accuracy: 0.4289 - val_loss: 1.5057 - val_accuracy: 0.4714\n",
      "Epoch 2/30\n",
      "253/253 [==============================] - 17s 68ms/step - loss: 1.5713 - accuracy: 0.5426 - val_loss: 0.9716 - val_accuracy: 0.6349\n",
      "Epoch 3/30\n",
      "253/253 [==============================] - 17s 67ms/step - loss: 1.3416 - accuracy: 0.5906 - val_loss: 0.8621 - val_accuracy: 0.6757\n",
      "Epoch 4/30\n",
      "253/253 [==============================] - 19s 74ms/step - loss: 1.1649 - accuracy: 0.6289 - val_loss: 0.8075 - val_accuracy: 0.7262\n",
      "Epoch 5/30\n",
      "253/253 [==============================] - 19s 76ms/step - loss: 1.0479 - accuracy: 0.6588 - val_loss: 0.7753 - val_accuracy: 0.7275\n",
      "Epoch 6/30\n",
      "253/253 [==============================] - 19s 76ms/step - loss: 0.9451 - accuracy: 0.6811 - val_loss: 0.7555 - val_accuracy: 0.7343\n",
      "Epoch 7/30\n",
      "253/253 [==============================] - 18s 70ms/step - loss: 0.8487 - accuracy: 0.7076 - val_loss: 0.7364 - val_accuracy: 0.7466\n",
      "Epoch 8/30\n",
      "253/253 [==============================] - 19s 75ms/step - loss: 0.7810 - accuracy: 0.7277 - val_loss: 0.7293 - val_accuracy: 0.7534\n",
      "Epoch 9/30\n",
      "253/253 [==============================] - 21s 81ms/step - loss: 0.7092 - accuracy: 0.7453 - val_loss: 0.7131 - val_accuracy: 0.7589\n",
      "Epoch 10/30\n",
      "253/253 [==============================] - 19s 75ms/step - loss: 0.6661 - accuracy: 0.7624 - val_loss: 0.7140 - val_accuracy: 0.7548\n",
      "Epoch 11/30\n",
      "253/253 [==============================] - 19s 74ms/step - loss: 0.6098 - accuracy: 0.7784 - val_loss: 0.6974 - val_accuracy: 0.7629\n",
      "Epoch 12/30\n",
      "253/253 [==============================] - 18s 71ms/step - loss: 0.5696 - accuracy: 0.7917 - val_loss: 0.6886 - val_accuracy: 0.7602\n",
      "Epoch 13/30\n",
      "253/253 [==============================] - 19s 75ms/step - loss: 0.5294 - accuracy: 0.8051 - val_loss: 0.6968 - val_accuracy: 0.7575\n",
      "Epoch 14/30\n",
      "253/253 [==============================] - 17s 69ms/step - loss: 0.4854 - accuracy: 0.8197 - val_loss: 0.6860 - val_accuracy: 0.7643\n",
      "Epoch 15/30\n",
      "253/253 [==============================] - 17s 69ms/step - loss: 0.4514 - accuracy: 0.8304 - val_loss: 0.6794 - val_accuracy: 0.7670\n",
      "Epoch 16/30\n",
      "253/253 [==============================] - 18s 73ms/step - loss: 0.4102 - accuracy: 0.8482 - val_loss: 0.6998 - val_accuracy: 0.7725\n",
      "Epoch 17/30\n",
      "253/253 [==============================] - 19s 73ms/step - loss: 0.3853 - accuracy: 0.8550 - val_loss: 0.6919 - val_accuracy: 0.7670\n",
      "Epoch 18/30\n",
      "253/253 [==============================] - 17s 67ms/step - loss: 0.3500 - accuracy: 0.8695 - val_loss: 0.6912 - val_accuracy: 0.7711\n",
      "Epoch 19/30\n",
      "253/253 [==============================] - 17s 68ms/step - loss: 0.3242 - accuracy: 0.8776 - val_loss: 0.7077 - val_accuracy: 0.7698\n",
      "Epoch 20/30\n",
      "253/253 [==============================] - 18s 72ms/step - loss: 0.2924 - accuracy: 0.8909 - val_loss: 0.7145 - val_accuracy: 0.7643\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(train_ds, epochs=max_epochs, validation_data=val_ds,\n",
    "                    callbacks=[early_stop_callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c7056ccc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6/6 [==============================] - 0s 36ms/step - loss: 0.7050 - accuracy: 0.7684\n",
      "test accuracy=0.768\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(val_ds)\n",
    "print(f'test accuracy={test_acc:.3f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "480afa8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc = history.history['accuracy']\n",
    "val_acc = history.history['val_accuracy']\n",
    "\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs_range = range(len(acc))\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(epochs_range, acc, label='Training Accuracy')\n",
    "plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
    "plt.legend(loc='lower right')\n",
    "plt.title('Training and Validation Accuracy')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(epochs_range, loss, label='Training Loss')\n",
    "plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
    "plt.legend(loc='upper right')\n",
    "plt.title('Training and Validation Loss')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
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